Exact limits of inference in coalescent models.
Bayes error rates
Coalescent
Effective population size
Sequentially Markov coalescent
Journal
Theoretical population biology
ISSN: 1096-0325
Titre abrégé: Theor Popul Biol
Pays: United States
ID NLM: 0256422
Informations de publication
Date de publication:
02 2019
02 2019
Historique:
received:
11
02
2018
revised:
12
11
2018
accepted:
27
11
2018
pubmed:
21
12
2018
medline:
23
5
2019
entrez:
21
12
2018
Statut:
ppublish
Résumé
Recovery of population size history from molecular sequence data is an important problem in population genetics. Inference commonly relies on a coalescent model linking the population size history to genealogies. The high computational cost of estimating parameters from these models usually compels researchers to select a subset of the available data or to rely on insufficient summary statistics for statistical inference. We consider the problem of recovering the true population size history from two possible alternatives on the basis of coalescent time data previously considered by Kim et al. (2015). We improve upon previous results by giving exact expressions for the probability of correctly distinguishing between the two hypotheses as a function of the separation between the alternative size histories, the number of individuals, loci, and the sampling times. In more complicated settings we estimate the exact probability of correct recovery by Monte Carlo simulation. Our results give considerably more pessimistic inferential limits than those previously reported. We also extended our analyses to pairwise SMC and SMC' models of recombination. This work is relevant for optimal design when the inference goal is to test scientific hypotheses about population size trajectories in coalescent models with and without recombination.
Identifiants
pubmed: 30571959
pii: S0040-5809(18)30024-8
doi: 10.1016/j.tpb.2018.11.004
pmc: PMC6541399
mid: NIHMS1030110
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
75-93Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM131404
Pays : United States
Informations de copyright
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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